CHI 2018 Workshop

Big data in education has fostered emergent fields like educational data mining (Baker & Yacef, 2009) and learning analytics (Siemens & Long, 2011). Simulations and educational videogames are obvious candidates for the application of these analytic methods, affording big data situated in meaningful learning contexts (Gee, 2003; Steinkuehler et al., 2012). In design of these educational games, clickstream analytics for core design, alpha usertesting, and final-stage adaptive play design play a key role in optimizing learner experience. This paper maps learning analytics methods to these learning game development phases. Leveraging these powerful analytic tools of visualization, association mining, and predictive modeling throughout the design process is key to supporting players in a user-adaptive, engaging play experience optimized for learning.

Communications in Computer and Information Science, 2020

The widespread use of computer-based learning environments and the rise of big data have positioned learning analytics as a fundamental component of educational technology. Learning analytics provides methods for capturing and assessing student behaviors. In game-based learning environments, however, the development and integration of learning analytics has not yet reached their full potential. Research thus far has focused on the specification of learning analytics frameworks, implementation of different techniques and methods for the collection of data, and the development of automated assessment tools. Unfortunately, much work overlooks the importance of strategic data collection and therefore risks basing decisions on flawed or incomplete data. In this paper, we present our library that seeks to capture data in the context of a serious game, designed to be compatible with the Experience API (xAPI) and implemented in the Unity 3D game engine. Through this work, we aim to emphasize and extend the use of learning analytics in serious games, simplify the production of data, and record events with educational value.

2021 IEEE Global Engineering Education Conference (EDUCON)

Data science applications in education are quickly proliferating, partially due to the use of LMSs and MOOCs. However, the application of data science techniques in the validation and deployment of serious games is still scarce. Among other reasons, obtaining and communicating useful information from the varied interaction data captured from serious games requires specific data analysis and visualization techniques that are out of reach of most non-experts. To mitigate this lack of application of data science techniques in the field of serious games, we present T-Mon, a monitor of traces for the xAPI-SG standard. T-Mon offers a default set of analysis and visualizations for serious game interaction data that follows this standard, with no other configuration required. The information reported by T-Mon provides an overview of the game interaction data collected, bringing analysis and visualizations closer to non-experts and simplifying the application of serious games.

Lecture Notes in Computer Science, 2019

Serious games adoption is increasing, although their penetration in formal education is still surprisingly low. To improve their outcomes and increase their adoption in this domain, we propose new ways in which serious games can leverage the information extracted from player interactions, beyond the usual post-activity analysis. We focus on the use of: (1) open data which can be shared for research purposes, (2) real-time feedback for teachers that apply games in schools, to maintain awareness and control of their classroom, and (3) once enough data is gathered, data mining to improve game design, evaluation and deployment; and allow teachers and students to benefit from enhanced feedback or stealth assessment. Having developed and tested a game learning analytics platform throughout multiple experiments, we describe the lessons that we have learnt when analyzing learning analytics data in the previous contexts to improve serious games.

Proceedings of the 11th International Conference on Computer Supported Education

Learning Analytics have become an indispensable element of education, as digital mediums are increasingly used within formal and informal education. Integrating specifications for learning analytics in non-traditional educational mediums, such as serious games, has not yet reached the level of development necessary to fulfil their potential. Though much research has been conducted on the issue of managing and extracting value from learning analytics, the importance of specifications, methods and decisions for the initial creation of such data has been somewhat overlooked. To this end, we have developed a custom library that implements the Experience API specification within the Unity 3D game engine. In this paper, we present this library, as well as a representative scenario illustrating the procedure of generating and recording data. Through this work we aim to expand the reach of learning analytics into serious games, facilitate the generation of such data in commercially popular development tools and identify significant events, with educational value, to be recorded.

AERA, 2014

A central challenge to educational videogame research is capturing salient in-game data on play and learning. ADAGE (Assessment Data Aggregator for Game Environments) is a click-stream data framework currently being developed by the Games+Learning+Society research center to facilitate standardized collection of embedded assessment data across games. ADAGE integrates core game design structures into a click-stream data (telemetry) schema, which is then seeded with context vital to informing learning analyses. These data can be used to identify patterns in play within and across players (using data mining and learning analytic techniques) as well as statistical methods for testing hypotheses that compare play to content models. Three provided analysis examples show this diversity, combining applied statistics with Markov modeling and classification tree visualization. Overall, ADAGE provides a standardized game telemetry framework with a rich, method-agnostic data yield, efficient enough to have scalability, and flexible enough to use across games.

Journal of Learning Analytics

The purpose of this special section is to collect in one place how data in game-based learning environments may be turned into valuable analytics for student assessment, support of learning, and/or improvement of the game, using existing or emerging empirical research methodologies from various fields, including computer science, software engineering, educational data mining, learning analytics, learning sciences, statistics, and information visualization. Four contributions form this special section, which will inspire future high-quality research studies and contribute to the growing knowledge base of learning analytics and game-based learning research and practice.

International Journal of Learning Technology, 2014

integrate GBA across other GLS games, we should be able to develop more robust hypotheses of which aspects of our games matter for learning, and how to better use games as both instruments and assessments for learning. GBA begins with designing a game around specific learning goals. GBA has emerged as the design strategy of the Games, Learning and Society development group 1 . Our strategy is to bring content experts, game developers and programmers, artists, educators and learning scientists together in a collaborative design process (as described in . These design partners work to match subject matter content that can be best expressed in particular video game genres. GLS design teams identify promising content chunks that may enhance the public understanding of a particular domain. Typically these content chunks, such as the cultivation of a stem cell culture (Progenitor X) 2 , the process through which a virus enters a cell (Virulent) 3 , or how implicit bias influences perceptions in professional settings (Fair Play) 4 are laden with technical vocabulary and embedded in larger domain constructions. The task of the game design group is to translate core content chunks into games that invite players to participate in the logic of the concepts as a condition for learning the terminology. The development of a GBA model is critical for the design team to determine the relation between the game flow and the content model, in other words, to understand whether players can access the content chunks through game play. Recent Research on Games, Learning, Assessment and Data. The GBA design is grounded in recent research in game-based learning, evidence-centered design (ECD), and education data mining (EDM). We use a game-based learning experience to implement a version of ECD's task/content/evidence model into the game design. We then collect patterns of click-stream data, as in EDM, to develop records of in-game player interaction that can be used as evidence for learning. Here we briefly review some of the core research ideas that led to our GBA design.

A central challenge to educational videogame research is capturing salient in-game data on play and learning. ADAGE (Assessment Data Aggregator for Game Environments) is a click-stream data framework currently being developed by the Games+Learning+Society research center to facilitate standardized collection of embedded assessment data across games. ADAGE integrates core game design structures into a click-stream data (telemetry) schema, which is then seeded with context vital to informing learning analyses. These data can be used to identify patterns in play within and across players (using data mining and learning analytic techniques) as well as statistical methods for testing hypotheses that compare play to content models. Three provided analysis examples show this diversity, combining applied statistics with Markov modeling and classification tree visualization. Overall, ADAGE provides a standardized game telemetry framework with a rich, method-agnostic data yield, efficient enough to have scalability, and flexible enough to use across games.